The domain of artifical neural networks took large steps forward when AlexNet [1]}, GoogleNet [2]} and VGGNet [3]} were introduced for the classification of large image sets. Several publications address industrial monitoring applications: Masci et al. used CNN for classification of steel defects, and Soukup et al. used CNN for photometric stereoscopic images  [4]}, [5]}. A region proposal network for real-time object detection was presented by Ren et al., while Ferguson et al. used CNNs and transfer learning to detect X-ray image defects  [6]}, [7]}. Furthermore, the use of CNNs for industrial surface anomaly inspection was explored by Staar et al. [8]}.
The first ones to introduce pixel-based segmentation was Long et al. [9]}, while Schlegl et al. published a work in which GANs for marker detection were used for unsupervised anomaly detection [10]}. A survey exploring GANs for anomaly detection was presented by Di Mattia et al. [11]}.
Methods for color translation between different photographic contexts were discussed in various related publications  [12]}, [13]}.
Translation of completely different image domains was first achieved by Isola et al., who presented the pix2pix architecture [14]}. The pix2pix architecture was the first to allow the projection of various image domains such as edge objects or label images to colored photographs.
A huge problem when dealing with pixel-based segmentation tasks is a big gap between a small foreground and a relatively large background. This is especially reflected in the case of carbon fiber suface images. Ronneberger et al. confirmed, that multi-channel feature maps prove to be more useful in these tasks [15]}. It should be noted that this architecture makes heavy use of data enhancement, while taking only little input data. However, its capabilities are limited when dealing with very small training sets.
Multiple approaches to use GANs for the specific purpose of data augmentation were presented in different works. Frid-Adar et al. and Mariani et al. were able to apply GANs to classification tasks, while Choi et al. presented an approach that makes use of image-to-image translation networks for semantic segmentation tasks by transforming labeled data to related image domains so that the original label still fits to the artificially created image  [16]}, [17]}, [18]}. Huang et al. utilized image-to-image networks for shared segmentation tasks by using multiple image domains for the training process [19]}.
The authors of this paper could not identify any publication dedicated to the use of GANs for generating entirely new image and label pairs for enhanced training datasets in the context of semantic image segmentation.
